Littles Law Technology Academy

  📌 Purpose Integrating performance testing into CI/CD pipelines ensures that performance issues are detected early and continuously , not just before release. 🎯 Key Concepts Shift-Left Testing Incorporate performance tests in development & QA phases. Detect bottlenecks before production deployment. Automated Test Execution Use CI/CD tools (Jenkins, GitHub Actions, GitLab CI, Azure DevOps). Trigger performance tests on build, merge, or scheduled intervals . Include baseline and regression tests . Parameterized & Scalable Tests Use environment variables or config files for multiple environments (dev, QA, staging). Enable dynamic scaling for cloud-based load generators. Integration with Monitoring & Reporting Push results to dashboards (Grafana, Kibana, Dynatrace). Compare metrics against baseline and SLA thresholds . Alert teams on performance regressions. Performance Regression Testing Validate new changes...

  📌 Purpose After identifying bottlenecks, optimization ensures the system meets performance SLAs under expected load. 🎯 Common Optimization Areas Application Code Optimize algorithms & data structures . Minimize unnecessary computations and loops. Reduce synchronous/blocking calls. Database Add or optimize indexes for slow queries. Use query caching where applicable. Optimize joins and avoid heavy subqueries. Connection pool tuning. Server & Infrastructure Vertical scaling : Increase CPU, memory. Horizontal scaling : Add more servers/load balancers. Enable caching layers (Redis, Memcached). Web/Application Layer Use CDN for static content. Enable compression (gzip) for responses. Minimize HTTP requests (bundle JS/CSS). Concurrency & Threading Tune thread pools, worker processes . Avoid thread starvation and deadlocks. Network & Middleware Optimize load balancer configuration . ...

  📌 Purpose Results analysis interprets the raw performance data to identify bottlenecks, verify SLA compliance, and provide actionable insights. Reporting communicates findings to stakeholders. 🎯 Key Analysis Steps Data Validation Ensure test ran successfully with no script or environment errors. Confirm monitoring metrics are complete and consistent . Analyze Metrics Response Time : Average, 90th percentile, maximum. Throughput : Transactions/sec, data processed/sec. Error Rates : HTTP errors, failed transactions. Resource Utilization : CPU, memory, disk I/O, network usage. Identify Bottlenecks Correlate high response times with high CPU, memory, DB, or network usage. Check for thread pool exhaustion , GC pauses, DB locks. Compare baseline vs peak load performance. Trend & Pattern Analysis Analyze load vs response time graphs for degradation points. Detect memory leaks via endurance tests. Spot throughput saturati...

  📌 Purpose The execution phase is where the workload is applied to the system , and data is collected for performance evaluation. A structured approach ensures repeatability and accuracy . 🎯 Steps in Test Execution Pre-Execution Checks Confirm environment readiness (servers, DB, services up). Ensure monitoring tools & log collection are active. Validate scripts with a sanity test (low load, short run). Baseline Test Execute with minimal load. Measure base response times, throughput, error rates . Acts as a benchmark for later comparisons. Dry Run (Shakeout Test) Run at ~20–30% load. Identify configuration gaps (firewall rules, DB connections, monitoring setup). Load Test Execution Apply workload as per model (ramp-up → steady-state → ramp-down). Observe system behavior in real-time (APM, infrastructure, logs). Stress, Spike & Endurance Tests Stress Test → Increase load until system breaks. Spike Test → Sud...

  📌 Purpose Monitoring during performance testing helps identify bottlenecks that impact system scalability, stability, and responsiveness. 🎯 Key Monitoring Areas Server-Side Monitoring CPU Usage : High utilization → CPU-bound system. Memory Usage : Memory leaks, garbage collection issues. Disk I/O : Slow reads/writes, storage saturation. Network Utilization : Bandwidth bottlenecks, packet loss. Application-Level Monitoring Thread/Connection Pools : Exhaustion causes errors/timeouts. Garbage Collection (GC) Metrics : Frequent GC pauses (Java/.NET apps). Error Rates : HTTP 5xx, DB errors, API failures. Database Monitoring Query Response Times : Slow SQL queries, missing indexes. Locks & Deadlocks : Concurrent transaction issues. Connection Pool Usage : Exhausted connections → blocked requests. Network & Infrastructure Monitoring Latency & Packet Loss : Impacts distributed systems. Load Balancers : Uneven distrib...

  📌 Purpose Workload modeling ensures tests mirror real-world usage . Execution design defines how the load is applied over time. 🎯 Workload Modeling Identify User Journeys Pick business-critical scenarios (e.g., login, search, checkout). Assign weights based on production analytics (e.g., 70% search, 20% checkout, 10% admin). Determine Load Profile Concurrent Users → Number of users active at once. Transactions Per Second (TPS) → Business-driven metric. Think Time & Pacing → Control realism of user actions. Workload Types Open Model → Requests arrive at a fixed rate (good for web traffic). Closed Model → Fixed number of concurrent users (good for enterprise apps). 🎯 Execution Design Ramp-Up Gradually increase load from 0 → target users. Avoid sudden spikes unless testing spike behavior. Steady-State (Sustained Load) Maintain target load for a defined period. Collect performance data under stable load con...

  📌 Purpose of Scripting Performance test scripts simulate real user behavior . Poor scripting → inaccurate results → misleading conclusions. 🎯 Key Best Practices Record and Enhance Record baseline script using tool recorder (JMeter, LoadRunner, NeoLoad). Remove unnecessary requests (ads, tracking pixels, third-party calls). Parameterization (Data-Driven Testing) Replace hardcoded values with dynamic test data (usernames, IDs, search terms). Prevents cache hits and improves realism. Correlation (Dynamic Value Handling) Capture and reuse session IDs, tokens, cookies from server responses. Essential for stateful applications (logins, transactions). Think Time Add delays between actions to mimic real user behavior . Avoids constant hammering that inflates load unrealistically. Pacing Control how frequently a user repeats a transaction. Balances throughput and realism. Reusable Functions / Modularization Create reusab...

  📌 Importance of Planning Without a proper plan, performance testing becomes random load generation. A clear strategy ensures relevance, accuracy, and stakeholder confidence . 🎯 Steps in Performance Test Planning Requirement Gathering Understand business objectives (e.g., system must support 10,000 concurrent users). Identify SLAs / KPIs (e.g., API response < 2 sec, error rate < 1%). Identify Performance Scenarios Business-critical flows (e.g., login, checkout, search). Peak load conditions (seasonal spikes, campaign launches). Data volume considerations (DB growth, caching). Workload Modeling Define user mix (e.g., 60% browsing, 30% checkout, 10% admin). Determine concurrency model (open vs closed workload). Set ramp-up, steady-state, ramp-down timelines. Environment Setup Production-like test environment. Isolate network dependencies. Configure monitoring tools. Test Data Preparation Unique test accounts,...

  📌 Popular Open Source Tools Apache JMeter Widely used, supports HTTP, JDBC, FTP, SOAP, REST, etc. Extensible with plugins. GUI + CLI (non-GUI for load execution). Gatling Scala-based, developer-friendly. Supports CI/CD pipelines. Locust Python-based, distributed load generation. Scripted in Python for flexibility. 📌 Commercial Tools LoadRunner (Micro Focus) Enterprise-grade, wide protocol support. Advanced analytics and monitoring. NeoLoad (Tricentis) Supports APIs, web, SAP, Citrix, mobile. Good CI/CD integration. BlazeMeter Cloud-based, JMeter-compatible. Scalable distributed testing. Dynatrace Synthetic / AppDynamics / New Relic Focused on monitoring + synthetic testing. 📌 Cloud-Native/Modern Options k6 – Modern scripting (JavaScript), CI/CD friendly. Azure Load Testing / AWS Distributed Load Testing – Cloud-native integration. Google Cloud Performance Testing (via k6/JMeter integ...

📌 Definition Performance Testing is a non-functional testing technique used to determine how a system performs under expected or peak workload conditions in terms of responsiveness, stability, scalability, and reliability . 🎯 Objectives Identify response time, throughput, and resource utilization . Detect bottlenecks (CPU, memory, I/O, network, DB). Establish a baseline for performance. Ensure the system meets SLAs (Service Level Agreements) . Validate scalability and stability . 🛠️ Key Performance Metrics Response Time (Latency) : Time taken to respond to a request. Throughput : Number of transactions processed per second/minute. Concurrent Users : Number of users accessing the system simultaneously. Error Rate : Percentage of failed requests. Resource Utilization : CPU, memory, disk I/O, network usage. 🔑 Types of Performance Testing (High-Level) Load Testing – Normal & peak load handling. Stress Testing – Beyond capacity (brea...

 Day 01 : 💡 Technical Tip of the Day: Kubernetes Made Simple Ever struggled to explain  Pods ,  ReplicaSets , and  Deployments  in Kubernetes? Here's a lightbulb analogy that makes it crystal clear: 🔹  Pod = Single Lightbulb The basic unit of work - one container (or a few tightly coupled ones) running together. 🔹  ReplicaSet = Light Switch Ensures a specific number of bulbs are always on. If one burns out, it automatically replaces it to maintain your desired count. 🔹  Deployment = Smart Home System The intelligent orchestrator that: ✅ Controls the number of bulbs (like ReplicaSet) ✅ Seamlessly upgrades to new, energy-efficient models (rolling updates) ✅ Rolls back to old bulbs if the new ones flicker (rollback capability) Why this matters:  Understanding these core concepts is essential for anyone working with containerized applications. Each layer adds more intelligence and automation to your infrastructure. What's your favorite analo...